

import argparse
import hashlib  # hashlib is only used inside the Test class
import struct



class 						_A     :





  '''simple docstring'''



  def __init__(  self   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		Optional[int]        =		data
      snake_case							:		Any        =		[0X6_7_4_5_2_3_0_1, 0Xe_f_c_d_a_b_8_9, 0X9_8_b_a_d_c_f_e, 0X1_0_3_2_5_4_7_6, 0Xc_3_d_2_e_1_f_0]





  @staticmethod
  def    snake_case_       (  SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      return ((n << b) | (n >> (32 - b))) & 0Xf_f_f_f_f_f_f_f





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		int        =		B"""\x80""" + B"""\x00""" * (63 - (len(self.data    ) + 8) % 64)
      snake_case							:		Tuple        =		self.data + padding + struct.pack(""">Q"""   ,8 * len(self.data    )    )
      return padded_data





  def    snake_case_       (  self    ):

      '''simple docstring'''
      return [
          self.padded_data[i : i + 64] for i in range(0   ,len(self.padded_data    )   ,64    )
      ]





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		Tuple        =		list(struct.unpack(""">16L"""   ,SCREAMING_SNAKE_CASE_    )    ) + [0] * 64
      for i in range(16   ,80    ):
          snake_case							:		List[Any]        =		self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 14] ^ w[i - 16])   ,1    )
      return w





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Dict        =		self.padding()
      snake_case							:		List[Any]        =		self.split_blocks()
      for block in self.blocks:
          snake_case							:		Tuple        =		self.expand_block(SCREAMING_SNAKE_CASE_    )
          snake_case      ,		snake_case      ,		snake_case      ,		snake_case      ,		snake_case							:		Optional[Any]        =		self.h
          for i in range(0   ,80    ):
              if 0 <= i < 20:
                  snake_case							:		Union[str, Any]        =		(b & c) | ((~b) & d)
                  snake_case							:		Optional[int]        =		0X5_a_8_2_7_9_9_9
              elif 20 <= i < 40:
                  snake_case							:		int        =		b ^ c ^ d
                  snake_case							:		List[str]        =		0X6_e_d_9_e_b_a_1
              elif 40 <= i < 60:
                  snake_case							:		str        =		(b & c) | (b & d) | (c & d)
                  snake_case							:		List[str]        =		0X8_f_1_b_b_c_d_c
              elif 60 <= i < 80:
                  snake_case							:		List[Any]        =		b ^ c ^ d
                  snake_case							:		int        =		0Xc_a_6_2_c_1_d_6
              snake_case      ,		snake_case      ,		snake_case      ,		snake_case      ,		snake_case							:		Any        =		(
                  self.rotate(SCREAMING_SNAKE_CASE_   ,5    ) + f + e + k + expanded_block[i] & 0Xf_f_f_f_f_f_f_f,
                  a,
                  self.rotate(SCREAMING_SNAKE_CASE_   ,30    ),
                  c,
                  d,
              )
          snake_case							:		Optional[Any]        =		(
              self.h[0] + a & 0Xf_f_f_f_f_f_f_f,
              self.h[1] + b & 0Xf_f_f_f_f_f_f_f,
              self.h[2] + c & 0Xf_f_f_f_f_f_f_f,
              self.h[3] + d & 0Xf_f_f_f_f_f_f_f,
              self.h[4] + e & 0Xf_f_f_f_f_f_f_f,
          )
      return ("{:08x}" * 5).format(*self.h    )







def       lowercase					(	)				->			List[Any]:





    '''simple docstring'''




    snake_case							:		List[Any]        =		b"""Test String"""
    assert SHAaHash(a_		).final_hash() == hashlib.shaa(a_		).hexdigest()  # noqa: S324







def       lowercase					(	)				->			Any:





    '''simple docstring'''




    snake_case							:		Dict        =		argparse.ArgumentParser(description="""Process some strings or files"""		)
    parser.add_argument(
        """--string""" ,    dest="""input_string""" ,    default="""Hello World!! Welcome to Cryptography""" ,    help="""Hash the string""" ,    )
    parser.add_argument("""--file""" ,    dest="""input_file""" ,    help="""Hash contents of a file"""		)
    snake_case							:		List[str]        =		parser.parse_args()
    snake_case							:		List[str]        =		args.input_string
    # In any case hash input should be a bytestring
    if args.input_file:
        with open(args.input_file ,    """rb"""		) as f:
            snake_case							:		List[str]        =		f.read()
    else:
        snake_case							:		Optional[int]        =		bytes(a_ ,    """utf-8"""		)
    print(SHAaHash(a_		).final_hash()		)


if __name__ == "__main__":
     main()
     import doctest

     doctest.testmod()









'''simple docstring'''


import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()





class  __lowercase    (	__magic_name__							,				unittest.TestCase			):
   _a    						=   PriorTransformer
   _a    						=   """hidden_states"""







   @property
   def 		UpperCamelCase__      (						self   )					->     int:
     __a								=       4
     __a								=       8
     __a								=       7

     __a								=       floats_tensor((batch_size, embedding_dim)   ).to(UpperCamelCase   )

     __a								=       floats_tensor((batch_size, embedding_dim)   ).to(UpperCamelCase   )
     __a								=       floats_tensor((batch_size, num_embeddings, embedding_dim)   ).to(UpperCamelCase   )

     return {
         "hidden_states": hidden_states,
         "timestep": 2,
         "proj_embedding": proj_embedding,
         "encoder_hidden_states": encoder_hidden_states,
     }







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase=0   )					->     Union[str, Any]:
     torch.manual_seed(UpperCamelCase   )
     __a								=       4
     __a								=       8
     __a								=       7

     __a								=       torch.randn((batch_size, embedding_dim)   ).to(UpperCamelCase   )

     __a								=       torch.randn((batch_size, embedding_dim)   ).to(UpperCamelCase   )
     __a								=       torch.randn((batch_size, num_embeddings, embedding_dim)   ).to(UpperCamelCase   )

     return {
         "hidden_states": hidden_states,
         "timestep": 2,
         "proj_embedding": proj_embedding,
         "encoder_hidden_states": encoder_hidden_states,
     }







   @property
   def 		UpperCamelCase__      (						self   )					->     List[str]:
     return (4, 8)







   @property
   def 		UpperCamelCase__      (						self   )					->     str:
     return (4, 8)







   def 		UpperCamelCase__      (						self   )					->     Dict:
     __a								=       {
         'num_attention_heads': 2,
         'attention_head_dim': 4,
         'num_layers': 2,
         'embedding_dim': 8,
         'num_embeddings': 7,
         'additional_embeddings': 4,
     }
     __a								=       self.dummy_input
     return init_dict, inputs_dict







   def 		UpperCamelCase__      (						self   )					->     List[Any]:
     __a						,							__a								=       PriorTransformer.from_pretrained(
         'hf-internal-testing/prior-dummy'   ,				output_loading_info=UpperCamelCase   )
     self.assertIsNotNone(UpperCamelCase   )
     self.assertEqual(len(loading_info['missing_keys']   )   ,				0   )

     model.to(UpperCamelCase   )
     __a								=       model(**self.dummy_input   )[0]

     assert hidden_states is not None, "Make sure output is not None"







   def 		UpperCamelCase__      (						self   )					->     Optional[Any]:
     __a						,							__a								=       self.prepare_init_args_and_inputs_for_common()

     __a								=       self.model_class(**UpperCamelCase   )
     __a								=       inspect.signature(model.forward   )
     # signature.parameters is an OrderedDict => so arg_names order is deterministic
     __a								=       [*signature.parameters.keys()]

     __a								=       ['hidden_states', 'timestep']
     self.assertListEqual(arg_names[:2]   ,				UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     str:
     __a								=       PriorTransformer.from_pretrained('hf-internal-testing/prior-dummy'   )
     __a								=       model.to(UpperCamelCase   )

     if hasattr(UpperCamelCase   ,				'set_default_attn_processor'   ):
       model.set_default_attn_processor()

     __a								=       self.get_dummy_seed_input()

     with torch.no_grad():
       __a								=       model(**UpperCamelCase   )[0]

     __a								=       output[0, :5].flatten().cpu()
     print(UpperCamelCase   )

     # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
     # the expected output slices are not the same for CPU and GPU.
     __a								=       torch.tensor([-1.3_436, -0.2_870, 0.7_538, 0.4_368, -0.0_239]   )
     self.assertTrue(torch_all_close(UpperCamelCase   ,				UpperCamelCase   ,				rtol=1e-2   )   )





@slow
class  __lowercase    (	unittest.TestCase			):







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase=1   ,				UpperCamelCase=768   ,				UpperCamelCase=77   ,				UpperCamelCase=0   )					->     List[str]:
     torch.manual_seed(UpperCamelCase   )
     __a								=       batch_size
     __a								=       embedding_dim
     __a								=       num_embeddings

     __a								=       torch.randn((batch_size, embedding_dim)   ).to(UpperCamelCase   )

     __a								=       torch.randn((batch_size, embedding_dim)   ).to(UpperCamelCase   )
     __a								=       torch.randn((batch_size, num_embeddings, embedding_dim)   ).to(UpperCamelCase   )

     return {
         "hidden_states": hidden_states,
         "timestep": 2,
         "proj_embedding": proj_embedding,
         "encoder_hidden_states": encoder_hidden_states,
     }







   def 		UpperCamelCase__      (						self   )					->     List[str]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()







   @parameterized.expand(
       [
           # fmt: off
           [13, [-0.5_861,  0.1_283, -0.0_931,  0.0_882,  0.4_476,  0.1_329, -0.0_498,  0.0_640]],
           [37, [-0.4_913,  0.0_110, -0.0_483,  0.0_541,  0.4_954, -0.0_170,  0.0_354,  0.1_651]],
           # fmt: on
       ]   )
   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase   )					->     Optional[Any]:
     __a								=       PriorTransformer.from_pretrained('kandinsky-community/kandinsky-2-1-prior'   ,				subfolder='prior'   )
     model.to(UpperCamelCase   )
     __a								=       self.get_dummy_seed_input(seed=UpperCamelCase   )

     with torch.no_grad():
       __a								=       model(**UpperCamelCase   )[0]

     assert list(sample.shape   ) == [1, 768]

     __a								=       sample[0, :8].flatten().cpu()
     print(UpperCamelCase   )
     __a								=       torch.tensor(UpperCamelCase   )

     assert torch_all_close(UpperCamelCase   ,				UpperCamelCase   ,				atol=1e-3   )



